For applications requiring detection support systems employing sensors, many performance requirements can be met only when data from multiple sensors or time sequenced measurements from a single sensor are combined. This process of combining data has been called sensor correlation and fusion, or simply data fusion. Data fusion can associate, correlate, and combine information from single and multiple sensor sources to determine a refined location and matched identity estimates for observed elements in an image, for example. It can employ advanced mathematical inference techniques to reduce false matches inferred from the data, reduce dependence on conditions, control for known or assumed inefficiencies in the data, and generally lead to a more reliable system of matching elements detected from a sensor in complex environments. The data fusion process can continuously refine the estimates and matching parameters and can evaluate the need for additional sensor data.
As the performance requirements increase (e.g. the demand for higher detection performance at lower false alarm rates) and targets become more difficult to detect (e.g. low observability), there is a greater demand to expand the dimensionality of information acquired—driving the need for multiple sensors and the combination of that data. This demand to expand the time and space dimensionality of sensor data adds at least two problems: (1) sensor data must be integrated and coordinated to maximize the overall system measurement process, and (2) processes are required to efficiently and accurately correlate and fuse data from a variety of sensors. As noted above, data fusion is a multilevel, multifaceted process dealing with, for example, the registration, detection, association, correlation, and combination of data and information from multiple sources to achieve a refined status and identity estimation, and with the complete and timely assessments of the environmental situation(s) involved with each data set (including targets and opportunities). Sensors produce individual observations or measurements (raw data) that must be placed in proper context first to create organized data sets (information) and then evaluated to infer higher-level meaning about the overall content in the information (knowledge).
In one example of the present invention, a data fusion system combines synthetic aperture radar (SAR) and hyperspectral (HS) data. The SAR and HS sensors produce time-sampled data. The SAR data is processed to form an image, and the HS data is processed to form multilayer imagery: these images are both registered and then combined to produce information in the form of an image database. The image database is evaluated to infer the details of interest to government and commercial customers associated with, for example, vegetation, human activities, facilities and targets as desired by the end user.
Current ISR analytical and data fusion processes often seek information from multiple sensors and sources in order to achieve improved inferences over those achieved from only a single sensor. For example, evaluating the outputs from a color camera and a radar system in theory provides twice as much data over just the camera alone, reduces uncertainty, reduces false positives and improves the overall amount of accurate information available for decision making.
Analytical and data fusion systems typically use a variety of algorithms and techniques to transform the sensor data in order to detect, locate, characterize, and identify objects and entities of interest such as geospatial location, vehicles, buildings, plant and equipment etc. These algorithmic techniques include signal and image processing, statistical estimation, pattern recognition and many other techniques (see D. L. Hall and R. J. Linn, “Algorithm selection for data fusion systems,” Proceedings of the 1987 Tri-Service Data Fusion Symposium, APL Johns Hopkins University, Laurel, Md., Vol. 1, pp. 100-110, June 1987).
As explained below, the present invention is an improvement over the past methods and systems as the analysis occurs in near real time, can produce even higher levels of probability of detecting a desired results, such as a detection event or monitoring data, and allows an operator to focus on the results of the fusion of data rather than on a specific sensor.